• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.379-379
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• 2020

The influence of drought has increased due to global warming. In addition, forest fires have occurred more frequently due to droughts and resulted in property losses and casualty. In this study, the effects of drought on Goseong-Sokcho Forest Fire in 2019 were analyzed using high-resolution synthetic precipitation data. In order to determine the severity of drought, the average, 20%tile and 80%ile values were calculated using the synthetic precipitation data of the past 30 years and compared with the current climatology. We have investigated the multi-year accumulated precipitation data to determine the persistence of drought. In Goseong-Sokcho forest fire case, the two-year cumulative synthetic precipitation data shows a similar value to the climate, but the three-year cumulative synthetic precipitation data was close to the 20%ile lines of the climate value. It may expose that the shortage of precipitation in 2017 had persisted until 2019, despite abundant precipitation during the summer in 2018. Therefore, Goseong-Sokcho forest fire might be spread more rapidly by drought which has been persisted since 2017.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.25-28
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• 2016

Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.252-252
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• 2011

Rainfall-runoff models are calibrated and validated by using a same data set such as observations. The past climate change effects the present rainfall pattern and also will effect on the future. To predict rainfall-runoff more preciously we have to consider the climate change pattern in the past, present and the future time. Thus, in this study, the climate change represents changes in mean precipitation and standard deviation in different patterns. In some river basins, there is no enough length of data for the analysis. Therefore, we have to generate the synthetic data using proper distribution for calculation of precipitation based on the observed data. In this study, Kajiyama model is used to analyze the runoff in the dry and the wet period, separately. Mean and standard deviation are used for generating precipitation from the gamma distribution. Twenty hypothetical scenarios are considered to show the climate change conditions. The mean precipitation are changed by -20%, -10%, 0%, +10% and +20% for the data generation with keeping the standard deviation constant in the wet and the dry period respectively. Similarly, the standard deviations of precipitation are changed by -20%, -10%, 0%, +10% and +20% keeping the mean value of precipitation constant for the wet and the dry period sequentially. In the wet period, when the standard deviation value varies then the mean NSE ratio is more fluctuate rather than the dry period. On the other hand, the mean NSE ratio in some extent is more fluctuate in the wet period and sometimes in the dry period, if the mean value of precipitation varies while keeping the standard deviation constant.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.634-645
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• 2011

A newly developed rainwater harvesting (RWH) system reliability model is evaluated for roof area of buildings in Haeundae District of Busan. RWH system is used to supply water for toilet flushing, back garden irrigation, and air cooling. This model is portable because it is based on a non-parametric precipitation generation algorithm using a markov chain. Precipitation occurrence is simulated using transition probabilities derived for each day of the year based on the historical probability of wet and dry day state changes. Precipitation amounts are selected from a matrix of historical values within a moving 30 day window that is centered on the target day. Then, the reliability of RWH system is determined for catchment area and tank volume ranges using synthetic precipitation data. As a result, the synthetic rainfall data well reproduced the characteristics of precipitation in Busan. Also the reliabilities of RWH system for each of demands were computed to high values. Furthermore, for study area using the RWH system, reduction efficiencies for rooftop runoff inputs to the sewer system and potable water demand are evaluated for 23%, 53%, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Magazine of the Korean Society of Agricultural Engineers
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• v.16 no.2
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• pp.3438-3453
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• 1974

Unmeasured value of water for human lives is widely approved, but the water as one of natural resources cannot be evaluated with ease since it changes itself ceaselessly by flowing-out or transforming the phase. Major objectives of the study concerned consequently with investigating its potentiality and evaluating its time seriesly availabity in a volumatic unit. And the study was performed to give the accurate original data to the planners concerned. Some developed rational methods of predicting runoff related to hydrological factors as precipitation, were to be discusseed for their theorical background and to be introduced whether they needed some corrections or not, comparing their estimation with actual runoff from synthetic unit-hydrograph methods. To do so, the study was performed to select Kongju Station, located at the watershed of the Keum River, and to collect such hydrological data from 1962 to 1972 as runoff, water level, precipitation, and so on. On the other hand, the hydrological characteristics of runoff were concluded more reasonably in numerical values, with calculating the the ratio of daily runoff to annual discharge of the flow in percentage, as. the distribution ratio of runoff. The results of the study can be summarized as follows; (1) There needed some consideration to apply the Kajiyama's Formula for predicting monthly runoff of rivers in Korea.(2) The rational methods of predicting runoff might be recommended to become less theorical and reliable than the unique analyzation of data concerned in each given water basin. The results from the Keum River prepared above would be available to any programms concerned. (3) The most accurate estimation for runoff could be suggested to synthetic unithydrograph methods calculated from the relation between each storm and runoff. However it was not contained in the study. (4) The relations between rainfall and runoff at KongJu Station were as following table. The table showed some intersting implications about the characteristics of runoff at site, which indicated that the runoff during three months from July to September approached total of 60% of quantity while precipitation concentrated on the other three from June to August. And there were some months which had more amount of runoff than expected values calculated from the precipitation, such as Febrary, March, August, September, Octover, and December, shown in the table. Such implications should be suggested to meet any correction factors in the future formulation concerned with the subjects, if any rational methods would be required.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.235-247
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• 2010

An environmental factor is very important in a war game model supporting military training. Most war game models in Korean armed forces apply the same weather conditions to all operation areas. As a result, it fails to derive a high-fidelity simulation result. For this reason this study attempts to develop factor techniques for a high-fidelity war game that can apply distributed synthetic atmospheric environment modeling data to a military simulation. The major developed factor technology of this study applies regional distributed precipitation data to the 2D-GIS based Simplified Detection Probability Model(SDPM) that was developed for this study. By doing this, this study shows that diversely distributed local weather conditions can be applied to a military simulation depending on the model resolution from theater level to engineering level, on the use from training model to analytical model, and on the description level from corps level to battalion level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.509-521
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• 1994

This study is an effort to develop computer simulation model that produce precipitation patterns from stochastic model. A stochastic model is formulated for the process of daily precipitation with considering the sequences of wet and dry days and the precipitation amounts on wet days. This study consists of 2 papers and the process of precipitation occurrence is modelled by an alternate renewal process (ARP) in paper (I). In the ARP model for the precipitation occurrence, four discrete distributions, used to fit the wet and dry spells, were as follows; truncated binomial distribution (TBD), truncated Poisson distribution (TPD), truncated negative binomial distribution (TNBD), logarithmic series distribution (LSD). In companion paper (II) the process of occurrence is developed by Markov chain. The amounts of precipitation, given that precipitation has occurred, are described by a Gamma. Pearson Type-III, Extremal Type-III, and 3 parameter Weibull distribution. Daily precipitation series model consists of two models, A-Wand A-G model, by combining the process of precipitation occurrence and a continuous probability distribution on the precipitation of wet days. To evaluate the performance of the simulation model, output from the model was compared with historical data of 7 stations in the Nakdong and Seomjin river basin. The results of paper (1) show that it is possible to design a model for the synthetic generation of IX)int precipitation patterns.

• The Journal of the Korean Society for Microbiology
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• v.22 no.2
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• pp.167-174
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• 1987

N-myc, a DNA sequence related to the oncogene c-myc, was found to be amplified in untreated primary neuroblastomas and the amplification appeared to be associated with advanced disease at diagnosis and rapid tumor progression. Synthetic peptides have been useful immunogens for generating antisera and monoclonal antibodies to a number of native proteins. In order to identify myc-related protein in the tumor cells, an antiserum against a synthetic hexapeptide (-Glu-Asp-Ile-Trp-Lys-Lys-), whose sequence corresponds to a part of the exon 2 of oncogene N-myc, was prepared by immunizing a rabbit with BSA-conjugated peptide. After ammonium sulfate precipitation and affinity column chromatography, it appeared to be specific to the peptide. Strong nuclear staining in immunoperoxidase method using this serum was observed in both human promyeloid leukemic cell line, HL-60(containing high c-myc copy number), and human neuroblastoma cell line, LA-N-5 (containing high N-myc copy number), whereas LA351 (human lymphoid cell line) cells did not react with the serum. This reaction was completely abrogated by incubating the antiserum with soluble excess peptide. These data suggest that the protein encoded by N-myc could be localized in the nucleus as c-myc protein and this antiserum can be used to detect myc-related tumor cells in clinical samples and to determine if the N-myc expression correlates with genomic amplification in cell lines, untreated primary tumors, and untreated metastases.